小小优化，不成敬意

run_analysis_en.py

@@ -63,13 +63,23 @@ def run_analysis(
     """
     Run the full AI-driven analysis pipeline.
     
+    Each run creates a timestamped subdirectory under output_dir:
+      output_dir/run_20260309_143025/
+        ├── analysis_report.md
+        └── charts/
+            ├── bar_chart.png
+            └── ...
+    
     Args:
         data_file: Path to any CSV file
         user_requirement: Natural language requirement (optional)
         template_file: Report template path (optional)
-        output_dir: Output directory
+        output_dir: Base output directory
     """
-    os.makedirs(output_dir, exist_ok=True)
+    # 每次运行创建带时间戳的子目录
+    run_timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+    run_dir = os.path.join(output_dir, f"run_{run_timestamp}")
+    os.makedirs(run_dir, exist_ok=True)
     config = get_config()
 
     print("\n" + "=" * 70)
@@ -77,6 +87,7 @@ def run_analysis(
     print("=" * 70)
     print(f"Start: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
     print(f"Data:  {data_file}")
+    print(f"Output: {run_dir}")
     if template_file:
         print(f"Template: {template_file}")
     print("=" * 70)
@@ -117,6 +128,7 @@ def run_analysis(
     # ── Stage 4: AI Task Execution ──
     print("\n[4/5] AI Executing Tasks...")
     # Reuse DAL from Stage 1 — no need to load data again
+    dal.set_output_dir(run_dir)
     results: List[AnalysisResult] = []
 
     sorted_tasks = sorted(analysis_plan.tasks, key=lambda t: t.priority, reverse=True)
@@ -137,12 +149,12 @@ def run_analysis(
 
     # ── Stage 5: Report Generation ──
     print("\n[5/5] Generating Report...")
-    report_path = os.path.join(output_dir, "analysis_report.md")
+    report_path = os.path.join(run_dir, "analysis_report.md")
 
     if template_file and os.path.exists(template_file):
-        report = _generate_template_report(profile, results, template_file, config)
+        report = _generate_template_report(profile, results, template_file, config, run_dir)
     else:
-        report = generate_report(results, requirement, profile)
+        report = generate_report(results, requirement, profile, output_path=run_dir)
 
     # Save report
     with open(report_path, 'w', encoding='utf-8') as f:
@@ -155,7 +167,7 @@ def run_analysis(
     print("\n" + "=" * 70)
     print("Analysis Complete!")
     print(f"End: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-    print(f"Output: {report_path}")
+    print(f"Output: {run_dir}")
     print("=" * 70)
 
     return True
@@ -165,7 +177,8 @@ def _generate_template_report(
     profile: DataProfile,
     results: List[AnalysisResult],
     template_path: str,
-    config
+    config,
+    run_dir: str = ""
 ) -> str:
     """Use AI to fill a template with data from task execution results."""
     client = OpenAI(api_key=config.llm.api_key, base_url=config.llm.base_url)
@@ -210,12 +223,17 @@ def _generate_template_report(
 {template}
 ```
 
+## 图表文件
+以下是分析过程中生成的图表文件，请在报告适当位置嵌入：
+{_collect_chart_paths(results, run_dir)}
+
 ## 要求
 1. 用实际数据填充模板中所有占位符
 2. 根据数据中的字段，智能映射到模板分类
 3. 所有数字必须来自分析结果，不要编造
 4. 如果某个模板分类在数据中没有对应，标注"本期无数据"
 5. 保持Markdown格式
+6. 在报告中嵌入图表，使用 ![描述](图表路径) 格式，让报告图文结合
 """
 
     print("  AI filling template with analysis results...")
@@ -243,6 +261,28 @@ def _generate_template_report(
     return header + report
 
 
+def _collect_chart_paths(results: List[AnalysisResult], run_dir: str = "") -> str:
+    """Collect all chart paths from task results for embedding in reports."""
+    paths = []
+    for r in results:
+        if not r.success:
+            continue
+        # From visualizations list
+        for viz in (r.visualizations or []):
+            if viz and viz not in paths:
+                paths.append(viz)
+        # From data dict (chart_path in tool results)
+        if isinstance(r.data, dict):
+            for key, val in r.data.items():
+                if isinstance(val, dict) and val.get('chart_path'):
+                    cp = val['chart_path']
+                    if cp not in paths:
+                        paths.append(cp)
+    if not paths:
+        return "(无图表)"
+    return "\n".join(f"- {p}" for p in paths)
+
+
 if __name__ == "__main__":
     import argparse
     parser = argparse.ArgumentParser(description="AI-Driven Data Analysis")